Jordahl et al. BMC Medical Genetics(2020) 21:228
https://doi.org/10.1186/s12881-020-01172-1

RESEARCH ARTICLE

Open AccessMediation by differential DNA methylation of
known associations between single nucleotide
polymorphisms and bladder cancer risk
Kristina M. Jordahl1,2* , Amanda I. Phipps1,2, Timothy W. Randolph2, Lesley F. Tinker2, Rami Nassir3, Lifang Hou4,
Garnet L. Anderson2, Karl T. Kelsey5, Emily White1,2 and Parveen Bhatti2,6Abstract
Background: Though bladder cancer has been the subject of many well-powered genome-wide association
studies, the mechanisms involving bladder-cancer-associated single nucleotide polymorphisms (SNPs) remain
largely unknown. This study focuses on rs798766, rs401681, rs2294008, and rs8102137, which have been associatedwith bladder cancer and are also cis-acting methylation quantitative loci (mQTL).
Methods: Among 412 bladder cancer cases and 424 controls from the Women’s Health Initiative (WHI), we
assessed whether the effects of these SNPs on bladder cancer are mediated through proximal DNA methylation
changes in pre-diagnostic blood at mQTL-associated CpG sites, which we refer to as natural indirect effects (NIEs).
We used a multiple-mediator mediation model for each of the four mQTL adjusted for matching variables andpotential confounders, including race/ethnicity, smoking status, and pack-years of smoking.
Results: While not statistically significant, our results suggest that substantial proportions of the modest
effects of rs401681 (ORNIE = 1.05, 95% confidence interval (CI) = 0.89 to 1.25; NIE percent = 98.5%) andrs2294008 (ORNIE = 1.10, 95% CI = 0.90 to 1.33; NIE percent = 77.6%) on bladder cancer risk are mediatedthrough differential DNA methylation at nearby mQTL-associated CpG sites. The suggestive results indicatethat rs2294008 may affect bladder cancer risk through a set of genes in the lymphocyte antigen 6 family,
which involves genes that bind to and modulate nicotinic acetylcholine receptors. There was no suggestive
evidence supporting mediation for rs8102137 and rs798766.
Conclusions: Though larger studies are necessary, the methylation changes associated with rs401681 andrs2294008 at mQTL-associated CpG sites may be relevant for bladder carcinogenesis, and this study
demonstrates how multi-omic data can be integrated to help understand the downstream effects of genetics
variants.
Keywords: Bladder cancer risk SNPs, DNA methylation, Bladder cancer, Mediation analysis* Correspondence: kjordahl@fredhutch.org
1
Department of Epidemiology, School of Public Health, University ofWashington, Box 357236, Seattle, WA 98195, USA
2
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center,Seattle, WA, USA
Full list of author information is available at the end of the article© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

Jordahl et al. BMC Medical Genetics(2020) 21:228

Introduction
Bladder cancer has been the subject of multiple wellpowered genome-wide association studies, which haveidentified a robust set of single nucleotide polymorphisms (SNPs) associated with bladder cancer risk [1].
As is the case for most complex diseases, the mechanisms underlying these associations remain largely unknown. Since changes in DNA methylation may
reveal shifts in epigenetic state or in gene expression,
we explored whether proximal changes in DNA
methylation are causally involved in the effects ofthese SNPs on bladder cancer risk to improve ourunderstanding of their carcinogenic downstream
effects.
Our study focuses on rs798766, rs401681, rs2294008,
and rs8102137. These SNPs have been associated withbladder cancer in genome-wide association studies andare also methylation quantitative loci (mQTL), since
they affect patterns of DNA methylation at nearby CpG
sites [2]. For rs2294008, we also follow up on preliminary evidence that the T risk allele has a stronger effect insmokers [3], where smoking is the most important
known risk factor for bladder cancer.
Based on queries of the Accessible Resource forIntegrated Epigenomic Studies (ARIES) mQTL database [4], 50% of the eight SNPs associated with bladder cancer were mQTL in blood, as compared toonly 34% of total SNPs across the entire genome.
This is consistent with genome-wide association results for other complex traits, which are often
enriched for cis mQTL [4], suggesting that mQTL
SNPs associated with complex diseases like bladdercancer may have effects that can be detected by differential DNA methylation. Our study formally investigates the potential mediation of the association
between mQTL SNPs and bladder cancer risk throughmethylation changes in blood at mQTL-associatedCpG sites. This study represents an important step
toward integrating genetic and epigenetic data andimproving our understanding of the mechanisms related to genetic variants involved in susceptibility to
bladder cancer.
Patients and methods
Study participants

The current study involved participants from a casecontrol study of pre-diagnostic DNA methylation andbladder cancer, which was nested in the Women’s
Health Initiative (WHI) [5]. Briefly, the 161,808 postmenopausal women in were recruited across the US
from 1993 to 1998 in the clinical trials of hormone
therapy, dietary modification, and calcium/vitamin D
supplementation and in the observational study arms
[6]. The WHI Extension Studies collected healthPage 2 of 9

outcomes information on consenting WHI participants after 2005.
The initial case-control study included 440 WHI
participants diagnosed with transitional cell carcinomaof the bladder during follow-up and 440 controls
without cancer. Cases and controls were matched on
year of enrollment, age at enrollment, follow-up time,trial component, and DNA extraction method [5].
Subjects were excluded if they had missing race/ethnicity (n = 1) or pack-years information (n = 35), or if
salt or Bioserve was used as the DNA extraction
method (n = 8), leaving a sample size of 412 cases and424 controls.
Data and biospecimen collection

Age and race/ethnicity was self-reported during thescreening process for the WHI [6]. The smoking information used in our analyses was collected through baseline questionnaires and used to determine smoking
status and pack-years of smoking [7].
DNA methylation data

Genome-wide DNA methylation was assessed based on
fasting blood samples collected at baseline and stored as
buffy coats at − 70 °C. Methylation M-values, which are
the base-2 logit of the methylated signal divided by the
total signal, were measured at each autosomal CpG siteusing the Illumina 450 K Infinium HumanMethylation
Bead Array [5, 8]. During pre-processing and quality
control, we applied background correction and functional normalization and excluded CpG sites based onprobe detection, beadcount, proximity to common SNPs,
and cross-reactivity [5].
Identifying mQTL SNPs associated with bladder cancer

A meta-analysis of bladder cancer genome-wide association studies conducted in 2014 combined all previous
studies to verify known loci and reported genome-wide
significant associations (p < 5e-8) for eight SNPs:
rs710521, rs798766, rs401681, rs1495741, rs2294008,
rs9642880, rs8102137, and rs1014971 [1]. The metaanalysis included an average of 22 studies involving an
average of 11,131 cases and 50,634 controls for each reported association. We then used the ARIES mQTL
database to assess whether each of these eight SNPs was
also an mQTL [4]. This publicly available dataset includes approximately 1000 mother-offspring pairs fromthe Avon Longitudinal Study of Parents and Children.
Associations between each of 8,282,911 directly genotyped or imputed common SNPs and each of 395,625
CpG sites on the Illumina Infinium HumanMethylation450 Bead Array that passed QC are provided in themQTL database. To most closely match the populationof older women included in the WHI, we only evaluatedJordahl et al. BMC Medical Genetics(2020) 21:228

the methylation data measured in peripheral blood samples collected from mothers at the middle age time point(mean age = 47.45; n = 742). For each SNP, we searched
for associated CpG sites in the MatrixEQTL database
and then restricted to SNP-CpG results with a p-value
below 1e-14 to control the false positive rate at 0.2% [4].
We also restricted to CpG sites that passed our quality
control steps, leaving four mQTL that were associatedwith methylation changes at a total of ten CpG sites.
Notably, the risk allele is the minor allele for all selected
SNPs except for rs401681.
SNP genotyping

Taqman® SNP Genotyping Assays were used to determine the genotypes for each mQTL SNP. For the assay,
5 ng of genomic DNA was aliquoted into 384-well plates
and dried down. Each assay was combined with Taqman® Genotyper master mix, run under universal PCR
conditions, and analyzed with the ABI 7900 HT Taqman® Real-Time instrument.
Prior to running the study samples, 90 samples
representing 30 parent-parent-child trios from a
population of Utah residents with European ancestry(CEPH) were genotyped to assess performance of the
genotyping assays. Assay accuracy was verified bycomparing genotypes to publicly available genotype
data for these samples from SNP500 Cancer [9] anddbSNP [10] and by assessing inheritance errors. As
another quality control step, two external control
samples from the HapMap project were included oneach plate to confirm reliability and reproducibility of
the genotyping across the study plates. Inter-plate duplicates were also included. Samples with weak signals
and outliers were repeated once. There was 100%concordance among the duplicates. All SNPs had agreater than 99% call rate and were in HardyWeinberg equilibrium.
Statistical analyses

Mediation analyses were conducted with each of thefour mQTL (rs798766, rs401681, rs2294008, andrs8102137) as the exposure, all corresponding CpG
sites as the mediators, and incident bladder cancer as
the outcome. We estimated causal effects using a
regression-based approach for dichotomous outcomes[11, 12], which is based on a counterfactual framework for causal inference. This method uses a logistic
regression model for the outcome (1) and a linear regression model for the mediator (2), where A denotes
the exposure, M denotes the mediator(s), and Y denotes the outcome of interest. a and m denote fixed
levels of A and M, respectively. The logistic regression model was fit among cases and controls (n =Page 3 of 9836) and the linear regression model was fit only
among controls (n = 424).
logitðPðY ¼ 1ja; m; cÞÞ ¼ θ0 þ θ1 a þ

XK

0
ðiÞ
θ mðiÞ þ θ4 c
i¼1 2

ð1Þ
h
i
0
ðiÞ
ðiÞ
ðiÞ
E M ðiÞ ja; c ¼ β0 þ β1 a þ β2 cð2Þfor i = 1, …, K mediator(s). The models were adjusted
for a vector of covariates, c, that included our matchingvariables and potential confounders. Specifically, we adjusted our analyses for WHI arm (hormone therapy/dietary modification, hormone therapy, dietary modification,observational study), age at baseline (continuous), yearof enrollment (continuous: 1994–1995 = 1, 1996 = 2,
1997 = 3, 1998 = 4), follow-up time (continuous), DNA
extraction method (5-prime, phenol), race/ethnicity(Asian/Pacific Islander, Black/African American, Hispanic, non-Hispanic White, other), smoking status(never, former, current), and pack-years of smoking(continuous).
Using the coefficients estimated from regression models(1) and (2), the following equations were used to estimate
the odds ratio (OR) per risk allele for the natural direct
effect (NDE) and the natural indirect effect (NIE) [11]:ORNDE ¼ expðθ1 Þ

ORNIE ¼ exp

XKðiÞ ðiÞ

β θi¼1 1 2Effect estimates and confidence intervals (CIs) were estimated using the bootstrapping approach based on 1000
bootstrap samples. The ORNDE captures the effect of the
exposure on the outcome that does not act through the
CpG site mediators, while the ORNIE captures the effectof the exposure on the outcome that acts through the
CpG site mediators. The total effect (ORTE) is the product of the ORNDE and ORNIE, although this relationshipis not exact when using the bootstrapping approach.
The method allows the effects of individual mediators tocancel one another, since each SNP affects its associatedCpG sites simultaneously. The NIE percent is the percent of the total effect attributed to the natural indirect
effect (ORNIE/ORTE).
The mediation analyses were conducted based on amacro developed by Valeri and VanderWeele [13] using
SAS® software (versions 9.3 and 9.4, SAS® Institute Inc.,
Cary, NC, USA). The macro was modified to produce
multiple-mediator effect estimates based on existingJordahl et al. BMC Medical Genetics(2020) 21:228

equations [11] and to provide bootstrap-based confidence
intervals. The code for the multiple-mediator macro used
for these analyses is available at: https://github.com/kristina-jordahl/vanderweele_mediation_analysis_with_multiple_mediators.

Results
Table 1 provides the distribution of demographic andclinical characteristics by case-control status for ourstudy population. Compared to controls, a larger proportion of cases were of White race/ethnicity, were past
Table 1 Distribution of demographic and clinical characteristics
among bladder cancer cases and controls from the Women's
Health Initiative
Cases (n = 412)

Controls (n = 424)OS

213 (52%)

218 (52%)

CT: HT and DM

24 (6%)

26 (6%)

CT: HT only

55 (13%)56 (13%)

CT: DM only

120 (29%)

124 (29%)

WHI Arm (N, %)Age (N, %)< 50–59

97 (24%)100 (24%)

60–69

194 (47%)203 (48%)

70–79+

121 (29%)

121 (28%)

Year of Enrollment (N, %)1994 - 1995

105 (25%)109 (26%)1996

125 (30%)

127 (30%)1997

114 (28%)

116 (27%)1998

68 (17%)72 (17%)

DNA Extraction Method (N, %)5-prime397 (96%)408 (96%)

Phenol15 (4%)

16 (4%)

Race/Ethnicity (N, %)Asian/Pacific Islander

4 (1%)

12 (3%)

Black/African American

24 (6%)

40 (9%)

Hispanic

7 (2%)

16 (4%)

Non-Hispanic White

375 (91%)349 (82%)

Other

2 (< 1%)7 (2%)

Smoking (N, %)Never Smoked

155 (38%)234 (55%)

Past Smoker

204 (49%)

172 (41%)

Current Smoker

53 (13%)18 (4%)

Pack-Years (N, %)Never Smoker

155 (38%)234 (55%)> 0 - < 30

145 (35%)

137 (32%)≥ 30

112 (27%)53 (13%)Abbreviations: OS Observational study; CT Clinical trials; HT Hormone therapy
clinical trial; DM Dietary modification clinical trial

Page 4 of 9

and current smokers, and had a heavier smoking historyof at least 30 pack-years.
For each mQTL, there was a range of one to fivemQTL-associated CpG sites (Table 2). All of our selected mQTL were cis-acting, since they were within 1
Mb of their associated CpG sites. According to ARIES,
these mQTL are common SNPs with risk allele frequencies ranging from 0.20 to 0.56 and are mostly located inor near CpG islands. These SNPs have effect sizes ranging from a 0.4 to 3% difference in median proportion
methylated between homozygote groups. These SNPs
are associated with an increase in methylation for three
CpG sites and a decrease in methylation for seven CpG
sites.
The results from the mediation analyses for rs798766,
rs401681, rs2294008, and rs8102137 are presented inTable 3. Only rs8102137 had a statistically significant
total effect, and none of the natural indirect effects
through differential methylation of mQTL-associatedCpG sites were statistically significant. However, our results suggest that almost all (NIE percent = 98.5%) of themodest effect of rs401681 on bladder cancer risk
(ORTE = 1.05) acts through cg27028750 and cg26209169(ORNIE = 1.05, 95% CI = 0.89 to 1.25). Similarly, a large
NIE proportion (NIE percent = 77.6%) suggests that the
total effect of rs2294008 on bladder cancer risk (ORTE =1.13) may act through cg06565975, cg03405983,
cg24023258, cg17888033, and cg17252645 (ORNIE = 1.10,
95% CI = 0.90 to 1.33). There was little suggestive evidence to support mediation through changes in DNA
methylation for the associations of rs8102137 andrs798766 with bladder cancer risk.
To address possible residual population stratification,
we also conducted a sensitivity analysis restricted towhite participants. This analysis led to similar results
and conclusions, though rs798766 had a weaker total effect estimate (1.02 vs. 1.10), while rs401681 has a stronger total effect estimate (1.13 vs. 1.05). Interestingly, theNIE percent was lower for rs401681 (40.2%), but
accounted for the entire total effect estimate forrs2294008 (over 100%).

Discussion
Our results suggest that the effects of rs798766 andrs8102137 on bladder cancer risk are unlikely to be mediated by changes in methylation, which is consistent
with the putative mechanisms underlying the carcinogenicity of these SNPs. rs798766 is in an intron of theencoding transforming, acidic coiled-coil containing protein 3 (TACC3) gene and has been previously linked toactivating somatic mutations in nearby fibroblast growth
factor receptor 3 (FGGR3) [14]. rs8102137 is located 6
kb upstream of the cyclin E1 gene (CCNE1) [15], which
controls cell cycle progression to the S-phase [16].

chr4:1734239

chr5:1322087

chr8:143761931

chr19:30296853

rs798766

rs401681

rs2294008

rs8102137

C (0.32)

T (0.49)

C (0.53)

T (0.23)

RA (RAF)

chr19:30303674
chr19:30303651

cg16836589

chr8:143858414
chr8:143867129

cg17888033
cg17252645

cg27475126

chr8:143858548
chr8:143781297

cg03405983
cg24023258

chr8:143823917

chr5:1316264

cg26209169
cg06565975

chr5:1349422

chr4:1768889

CpG Location

cg27028750

cg00006948

CpG

5’UTR/1stExon
Body
1stExon/Body
1stExon/Body

LYNX1
LY6D
CCNE1
CCNE1

5’UTR/TSS200/1stExon
TSS1500

LYNX1
LY6K

TSS200CpG Gene Groupa

SLURP1CpG Gene

IslandIslandIsland

N_ShoreIslandS_Shelf

S_Shelf

IslandRelation to CpG Islandb

–

–

–

–

–

+

0.049

0.011

0.042

0.020

0.004

0.009

0.041

0.038

–
–

0.059

0.004

ARIES Effect Sized++

ARIES DirectioncaFunctional region of gene as indicated in v1.2 Illumina annotation: TSS1500 = 200–1500 bases upstream of the transcription start site; TSS200 = 0–200 bases upstream of the transcription start site; 5’UTR = 5 prime
untranslated region; 1stExon = First segment of gene coding for peptide sequence; Body = Between ATG and stop codon; 3’UTR = Between stop codon and poly A signal; Multiple listings indicate locus is in a region
with multiple splice variantsb
Position relative to CpG island as indicated in v1.2 Illumina annotation: Island = CpG island (CG content > 50%, Obs/Exp CpG ratio > 0.60, and length > 200 bps); OpeanSea = Non-island region; Shore = 0–2 kb flanking
CpG island; Shelf = 2–4 kb flanking CpG islandcDirection of association between mQTL and CpG site reported in the ARIES mQTL databasedEffect size (defined as the difference in median proportion methylated between homozygote groups) between mQTL and CpG site reported in the ARIES mQTL database
Abbreviations: RA Risk allele; RAF Risk allele frequency

SNP Location

SNP

Table 2 Selected mQTL and their associated CpG sites

Jordahl et al. BMC Medical Genetics(2020) 21:228
Page 5 of 9

Jordahl et al. BMC Medical Genetics(2020) 21:228

Page 6 of 9

Table 3 Mediating effects of mQTL-associated CpG site(s) for association between each SNP and bladder cancer risk
SNP

RegionGene

RAMediator(s)Effect of SNPa

OR

95% CI

rs798766

4p16.3

TMEM129-TACC3-FGFR3

T

cg00006948

NDE

1.16

(0.86, 1.50)

NIE

0.95

(0.81, 1.08)

TE

1.10

(0.85, 1.38)

NDE

1.01

(0.76, 1.33)

NIE

1.05

(0.89, 1.25)

TE

1.05

(0.85, 1.30)

NDE

1.04

(0.78, 1.35)

NIE

1.10

(0.90, 1.33)

TE

1.13

(0.91, 1.38)

NDE

1.36

(1.05, 1.74)

rs401681

rs2294008

rs8102137

5p15.33

8q24.3

19q12

TERT-CLPTML

PSCA

CCNE1

C

T

C

cg27028750 cg26209169

cg06565975 cg03405983
cg24023258 cg17888033
cg17252645
cg16836589 cg27475126

NIE

1.02

(0.93, 1.11)

TE

1.38

(1.09, 1.74)

aMediation models were adjusted for race/ethnicity, smoking status, pack-years of smoking, WHI arm, age at baseline, year of enrollment, follow-up time, and DNA
extraction method
Abbreviations: RA Risk allele; CI Confidence interval; NDE Natural direct effect; NIE Natural indirect effect; TE Total effectInstead of mediation through methylation changes,
rs8102137 may influence CCNE1 splicing, since it is associated with increased expression of a CCNE1 transcript lacking exon 7 in bladder tissue [17].
We found suggestive evidence that most of the effectof rs401681 on bladder cancer risk occurred indirectly
through methylation changes at mQTL-associated CpG
sites. The rs401681 SNP is in an intron of the cisplatin
resistance related protein CRR9p (CLPTM1L) gene. The
effect of rs401681 may primarily occur through an increase in methylation at cg27028750, which is not located within a known gene or CpG island. However,cg27028750 is located in a long terminal repeat element(MER-50), and genomic repeats are often targets for
Polycomb repression [18].
Previous research supports a lack of association between rs401681 and differential gene expression [19, 20].
However, it is possible that rs401681 is associated withcancer-related epigenetic switching, where the repression of genes related to early development and cellular
determination shifts from reversible silencing by Polycomb repressive complexes to more permanent silencing
by DNA methylation [21–24]. This switch is not expected to cause changes in gene expression, but instead
affects epigenetic plasticity [21]. Recent research suggests these changes predispose cells to cancer, possibly
by maintaining a stem-cell-like phenotype that initiates
abnormal growth and malignant transformation [24].
rs2294008 is located in the prostate stem cell antigen(PSCA) gene, and PSCA is a GPI-anchored cell surface
antigen in the Ly6 family [25]. Interestingly, thers2294008 variant has been shown to create an alternative translation start site for PSCA that extends the signal peptide from 11 to 20 amino acids [26]. We

observed that a substantial portion of the effect of thers2294008 T allele on bladder cancer risk may occur
through methylation changes at multiple mQTLassociated CpG sites, which are annotated to four othergenes in the Ly6 cluster on chromosome 8 [27]. These
genes include lymphocyte antigen 6 family member K(LY6K), lymphocyte antigen 6 family member D (LY6D),
Ly6/Neurotoxin 1 (LYNX1), and secreted Ly6/uPAR related protein 1 (SLURP1). LY6K and LY6D are consistently upregulated across many cancers [28] and show
increased expression in bladder cancer [28]. In particular, LY6K has been implicated in cell growth, migration,and invasion in bladder cancer cell lines [29], and its increased expression has been associated with reduced
five-year overall survival in bladder cancer patients [28,30].
rs2294008 may also affect bladder cancer through apathway that involves LYNX1 and SLURP1. Recent research suggests that PSCA, LYNX1, and SLURP1 bind to[31, 32] and modulate [27] the α7 subunit of nicotinic
acetylcholine receptors [33, 34]. As a result,methylation-mediated changes in LYNX1 and SLURP1
expression might be particularly relevant amongsmokers by promoting the well-established connection
between nicotine and bladder tumorigenesis throughcascades triggered by nicotinic acetylcholine receptors
that promote cell proliferation and survival [27, 35, 36].
Our study examines possible mediation throughmethylation for top bladder cancer genome-wide association study hits that are also mQTL. We leveraged information from previous genome-wide association studies
and an existing mQTL database and used pre-diagnostic
blood to explore the mechanisms underlying known associations between genetic variants and bladder cancer

Jordahl et al. BMC Medical Genetics(2020) 21:228

Page 7 of 9

Fig. 1 Causal directed acyclic graph shown as an illustration for a SNP with two mQTL-associated CpG site mediators, where solid arrows
correspond to the indirect effect through the mediator. For this study, SNPs rs798766, rs401681, rs2294008, and rs8102137 have one to fivemQTL-associated CpG site mediators

risk. We were able to replicate the previously reported
associations between our selected genetic variants andbladder cancer (ORTE estimates) [1] as well as the direction of the SNP-CpG associations reported in the ARIESmQTL database. Through this study, we also contributenew software to the research community because ourapproach required the adaptation of existing software to
apply a multiple-mediator approach that was well described but, to our knowledge, lacked a publicly available
implementation. Our sample size was not ideal for
examining genetic effects, since SNPs have relatively
small effect sizes. As a result, only rs8102137 had a statistically significant total effect. We were also limited by
the ARIES sample size, which may not have been large
enough to detect all relevant mQTL-associated mediators. Though we would expect SNPs in high linkage disequilibrium to have similar associations with mQTLassociated CpG sites and bladder cancer, we have used
the top associations as proxies for the causal variants,
which may have impacted our ability to detect causal effects. Despite these limitations, our results provide suggestive evidence of indirect effects through changes in
DNA methylation that warrant further exploration inlarger-scale studies of bladder cancer.
Conditional on the baseline covariates, the assumptions of mediation analyses require no-confounding of
theexposure-outcome,mediator-outcome,and
exposure-mediator relationships, and also require that
there are no mediator-outcome confounders caused by
the exposure. Analyses involving genetic variants are lessprone to confounding because, aside from race/ethnicity,
genotype is likely unaffected by most lifestyle factors
relevant for methylation or bladder cancer. We also adjusted for a comprehensive set of covariates to address
mediator-outcome confounding. We intentionally excluded cell type composition from the adjustment covariates, since it is another possible mediator that

contributes to the indirect effects in our analyses. However, our analysis is limited by the accuracy of our causal
model, which assumes the causal relationships depicted
in Fig. 1 and may be refined in the future as more is discovered about the mechanisms underlying known associations between mQTL GWAS hits and bladder cancer.
We also assumed that there was no interaction between the mQTL SNP and its mQTL-associated mediators. We note that there are potential, though nonsignificant, interactions between rs798766 andcg00006948 and between rs2294008 and cg03405983
that might improve these models in more flexible approaches. However, the inclusion of an interaction termin single-mediator mediation models produced the sameconclusions for rs798766 and rs2294008 in the main andrelevant exploratory analyses (results not shown).
As another possible source of bias, we acknowledge
error in our measurement of methylation because weonly have a single measure of DNA methylation at baseline, which may imprecisely capture the methylationlevels for the period that is etiologically relevant for ourcases. However, the error in our measures of methylation is likely to be non-differential and is expected to attenuate the reported indirect effect estimates.
Since DNA methylation can be tissue specific, ideally,
we would have conducted these analyses based on
methylation data from normal bladder tissue rather thanfrom blood. However, in a previous study of blood andbrain tissue, substantial overlap was observed for mQTL
from the two sites [2], indicating that at least some SNPCpG relationships may be consistent across tissue types.
Our study suggests that a substantial proportion of theeffects of rs401681 and of rs2294008 on bladder cancer
risk may be mediated through methylation changes at
nearby CpG sites. If confirmed by larger-scale studies,
our results point to a connection between rs401681 in
CLPTM1L and a possible nearby repression shift marked

Jordahl et al. BMC Medical Genetics(2020) 21:228

by cg27028750. A robust link between rs2294008 andrisk of bladder cancer through cg24023258 may identify
LY6K expression as a serum biomarker of bladder cancer
susceptibility in non-smokers. Among smokers, further
investigation of the role of rs2294008 in α7 nicotinic
acetylcholine receptor signaling is warranted. Overall,this study suggests highly plausible mechanisms bywhich known bladder cancer risk variants are associatedwith susceptibility to bladder cancer and demonstrates
the promising value of combining SNP and biomarker
data.

Conclusions
Though larger studies are necessary, the methylation
changes associated with rs401681 and rs2294008 at
mQTL-associated CpG sites may be relevant for bladdercarcinogenesis, and this study demonstrates how multiomic data can be integrated to help understand thedownstream effects of genetics variants.
Abbreviations
SNPs: Single nucleotide polymorphisms; mQTL: Methylation quantitative loci;
ARIES: Accessible resource for integrated epigenomic studies; WHI: Women’s
health initiative; OR: Odds ratio; NDE: Natural direct effect; NIE: Naturalindirect effect; TE: Total effectAcknowledgements
We would like to acknowledge Xiaoling Song for performing the bisulfite
conversion and plating of DNA samples and Cassandra Sather for running
the DNA methylation assays.
Authors’ contributions
PB conceived of and designed the case-control study and acquired the DNA
methylation data. KMJ created new software, analyzed and interpreted thedata, and drafted the manuscript. AIP, TWR, LFT, RN, LH, GLA, KTK, EW, andPB substantively revised the manuscript. All authors read and approved thefinal manuscript.
Funding
This study was supported by the American Cancer Society under Grant
125299-RSG-13-100-01-CCE; and Kristina M. Jordahl was also supported by
the National Cancer Institute (NCI) at the National Institutes of Health (NIH)
under Training Grants R25 CA094880 and T32 CA094880 for the data analysis,
interpretation of data, and writing of the manuscript. The contents of thiswork are solely the responsibility of the authors and do not necessarily represent the official views of the NCI, NIH.
The WHI program collected the questionnaire data and specimens for thecurrent study and was funded by National Heart, Lung, and Blood Institute,
National Institutes of Health, U.S. Department of Health and Human Services
through contracts HHSN268201600018C, HHSN268201600001C,HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C.
Availability of data and materials
The datasets generated and/or analyzed during the current study areavailable in the WHI Data and Specimen repository. The data are madepublicly available upon request and according to WHI policy. The ARIESmQTL database used to identify mQTL SNPs is available online at http://
www.mqtldb.org.
Ethics approval and consent to participate
All participants provided written informed consent to participate in the WHI[6]. This case-control study of pre-diagnostic DNA methylation and bladdercancer was nested in the WHI (Ancillary Study 311) and approved by theFred Hutchinson Cancer Research Center Institutional Review Board.

Page 8 of 9Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
Department of Epidemiology, School of Public Health, University ofWashington, Box 357236, Seattle, WA 98195, USA. 2Division of Public Health
Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
3
Department of Biochemistry and Molecular Medicine, University ofCalifornia, Davis, CA, USA. 4Department of Preventive Medicine, Northwestern
University Feinberg School of Medicine, Chicago, IL, USA. 5Departments of
Epidemiology and Pathology and Laboratory Medicine, Brown University,
Providence, RI, USA. 6Cancer Control Research, BC Cancer, Vancouver, BC,
Canada.
1

Received: 21 April 2020 Accepted: 12 November 2020

References
1. Figueroa JD, Ye Y, Siddiq A, Garcia-Closas M, Chatterjee N, Prokunina-Olsson
L, et al. Genome-wide association study identifies multiple loci associatedwith bladder cancer risk. Hum Mol Genet. 2014;23:1387–98.
2. Smith AK, Kilaru V, Kocak M, Almli LM, Mercer KB, Ressler KJ, et al.
Methylation quantitative trait loci (meQTLs) are consistently detected acrossancestry, developmental stage, and tissue type. BMC Genomics. 2014;15:145.
3. Wang S, Tang J, Wang M, Yuan L, Zhang Z. Genetic variation in PSCA andbladder cancer susceptibility in a Chinese population. Carcinogenesis. 2010;
31:621–4.
4. Gaunt TR, Shihab HA, Hemani G, Min JL, Woodward G, Lyttleton O, et al.
Systematic identification of genetic influences on methylation across thehuman life course. Genome Biol. 2016;17:61.
5. Jordahl KM, Randolph TW, Song X, Sather CL, Tinker LF, Phipps AI, et al.
Genome-wide DNA methylation in pre-diagnostic blood and bladdercancer risk in the women’s health initiative. Cancer Epidemiol Prev Biomark.
2018;27(6):689–95.
6. Hays J, Hunt JR, Hubbell FA, Anderson GL, Limacher M, Allen C, et al. The
Women’s health initiative recruitment methods and results. Ann Epidemiol.
2003;13:S18–77.
7. Jordahl KM, Phipps AI, Randolph TW, Tindle HA, Liu S, Tinker LF, et al.
Differential DNA methylation in blood as a mediator of the association
between cigarette smoking and bladder cancer risk among
postmenopausal women. Epigenetics. 2019;14:1065–73.
8. Dedeurwaerder S, Defrance M, Bizet M, Calonne E, Bontempi G, Fuks F. A
comprehensive overview of Infinium HumanMethylation450 dataprocessing. Brief Bioinform. 2014;15:929–41.
9. Packer BR, Yeager M, Staats B, Welch R, Crenshaw A, Kiley M, et al.
SNP500Cancer: a public resource for sequence validation and assay
development for genetic variation in candidate genes. Nucleic Acids Res.
2004;32:D528–32.
10. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al.
dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:
308–11.
11. VanderWeele T, Vansteelandt S. Mediation analysis with multiple mediators.
Epidemiol Methods. 2014;2:95–115.
12. VanderWeele TJ, Vansteelandt S. Odds ratios for mediation analysis for adichotomous outcome. Am J Epidemiol. 2010;172:1339–48.
13. Valeri L, VanderWeele TJ. Mediation analysis allowing for exposure–
mediator interactions and causal interpretation: theoretical assumptions
and implementation with SAS and SPSS macros. Psychol Methods. 2013;
18:137–50.
14. Kiemeney LA, Sulem P, Besenbacher S, Vermeulen SH, Sigurdsson A,
Thorleifsson G, et al. A sequence variant at 4p16.3 confers susceptibility tourinary bladder cancer. Nat Genet. 2010;42:415–9.
15. Kohaar I, Scott-Johnson A, Fu Y-P, Porter-Gill P, Prokunina-Olsson L.
Functional exploration of CCNE1 splicing forms as a possible link to bladdercancer susceptibility. Genome Biol. 2011;12:P7.
16. Siu KT, Rosner MR, Minella AC. An integrated view of cyclin E function andregulation. Cell Cycle. 2012;11:57–64.

Jordahl et al. BMC Medical Genetics(2020) 21:228

17. Fu YP, Kohaar I, Tang W, Porter-Gill P, Figueroa JD, Garcia-Colsas M et al.
Bladder cancer susceptibility variants within CCNE1 are associated withmRNA expression of an alternative splicing form. In: ASHG 2013 Annual
Meeting 2013.
18. Leeb M, Pasini D, Novatchkova M, Jaritz M, Helin K, Wutz A. Polycomb
complexes act redundantly to repress genomic repeats and genes. Genes
Dev. 2010;24:265–76.
19. Rafnar T, Sulem P, Stacey SN, Geller F, Gudmundsson J, Sigurdsson A, et al.
Sequence variants at the TERT-CLPTM1L locus associate with many cancer
types. Nat Genet. 2009;41:221–7.
20. Pintarelli G, Cotroneo CE, Noci S, Dugo M, Galvan A, Carpini SD, et al.
Genetic susceptibility variants for lung cancer: replication study andassessment as expression quantitative trait loci. Sci Rep. 2017;7:42185.
21. Gal-Yam EN, Egger G, Iniguez L, Holster H, Einarsson S, Zhang X, et al.
Frequent switching of Polycomb repressive marks and DNA
hypermethylation in the PC3 prostate cancer cell line. Proc Natl Acad Sci U
S A. 2008;105:12979–84.
22. Jones PA, Wolkowicz MJ, Rideout WM, Gonzales FA, Marziasz CM, Coetzee
GA, et al. De novo methylation of the MyoD1 CpG island during theestablishment of immortal cell lines. Proc Natl Acad Sci U S A. 1990;87:
6117–21.
23. Schlesinger Y, Straussman R, Keshet I, Farkash S, Hecht M, Zimmerman J,
et al. Polycomb-mediated methylation on Lys27 of histone H3 pre-marks
genes for de novo methylation in cancer. Nat Genet. 2007;39:232–6.
24. Widschwendter M, Fiegl H, Egle D, Mueller-Holzner E, Spizzo G, Marth C,
et al. Epigenetic stem cell signature in cancer. Nat Genet. 2007;39:157–8.
25. Reiter RE, Gu Z, Watabe T, Thomas G, Szigeti K, Davis E, et al. Prostate stem
cell antigen: a cell surface marker overexpressed in prostate cancer. Proc
Natl Acad Sci U S A. 1998;95:1735–40.
26. Kohaar I, Porter-Gill P, Lenz P, Fu Y-P, Mumy A, Tang W, et al. Genetic
variant as a selection marker for anti–prostate stem cell antigen
immunotherapy of bladder Cancer. JNCI J Natl Cancer Inst. 2013;105:69–73.
27. Fu XW, Song PF, Spindel ER. Role of Lynx1 and related Ly6 proteins as
modulators of cholinergic signaling in Normal and neoplastic bronchial
epithelium. Int Immunopharmacol. 2015;29:93–8.
28. Luo L, McGarvey P, Madhavan S, Kumar R, Gusev Y, Upadhyay G. Distinct
lymphocyte antigens 6 (Ly6) family members Ly6D, Ly6E, Ly6K and Ly6H
drive tumorigenesis and clinical outcome. Oncotarget. 2016;7:11165–93.
29. de Nooij-van Dalen AG, van Dongen GAMS, Smeets SJ, Nieuwenhuis EJC,
Stigter-van Walsum M, Snow GB, et al. Characterization of the human Ly-6
antigens, the newly annotated member Ly-6K included, as molecularmarkers for head-and-neck squamous cell carcinoma. Int J Cancer. 2003;103:768–74.
30. Lee J-S, Leem S-H, Lee S-Y, Kim S-C, Park E-S, Kim S-B, et al. Expression
signature of E2F1 and its associated genes predict superficial to invasive
progression of bladder tumors. J Clin Oncol Off J Am Soc Clin Oncol. 2010;
28:2660–7.
31. Ibañez-Tallon I, Miwa JM, Wang H-L, Adams NC, Crabtree GW, Sine SM, et al.
Novel modulation of neuronal nicotinic acetylcholine receptors by association
with the endogenous Prototoxin lynx1. Neuron. 2002;33:893–903.
32. Moriwaki Y, Yoshikawa K, Fukuda H, Fujii YX, Misawa H, Kawashima K.
Immune system expression of SLURP-1 and SLURP-2, two endogenous
nicotinic acetylcholine receptor ligands. Life Sci. 2007;80:2365–8.
33. Kim HS, Park WJ, Park EY, Koh JS, Hwang T-K, Kim JC. Role of nicotinic
acetylcholine receptor α3 and α7 subunits in detrusor Overactivity inducedby partial bladder outlet obstruction in rats. Int Neurourol J. 2015;19:12–8.
34. Beckel JM, Kanai A, Lee S-J, de Groat WC, Birder LA. Expression of functional
nicotinic acetylcholine receptors in rat urinary bladder epithelial cells. Am J
Physiol Renal Physiol. 2006;290:F103–10.
35. Singh S, Pillai S, Chellappan S. Nicotinic acetylcholine receptor signaling intumor growth and metastasis. J Oncol. 2011;2011. https://doi.org/10.1155/
2011/456743.
36. Chen R-J, Ho Y-S, Guo H-R, Wang Y-J. Rapid activation of Stat3 and ERK1/2
by nicotine modulates cell proliferation in human bladder cancer cells.
Toxicol Sci Off J Soc Toxicol. 2008;104:283–93.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

Page 9 of 9